In recent years, mitigation of the anthropogenic climate impact has become increasingly more important. Over the past decades, the aviation industry has grown significantly, making it a large contributor to greenhouse gases in the atmosphere. One such greenhouse gas is Ozone (O3) in the troposphere, which has a warming effect on the global climate. Aviation Nitrogen Oxides (NOx) emissions play a role in the formation and loss of O3 in the troposphere, however, the chemistry is highly non-linear which makes mitigation more complex. To gain a better understanding of the contribution of aviation to tropospheric O3, this thesis looks at how aviation’s tropospheric O3 contribution varies and how the Ozone Burden Efficiency (OBE) metric can be used to better explain these global variations. The OBE is a metric which quantifies the efficiency of the background chemistry and the global transportation phenomena to promote the net formation of O3. To conduct this research, chemistry climate simulation results of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model were used for a period of 2003 to 2018.

The analysis showed that across the simulated time period aviation’s contribution to tropospheric O3 increased from about 1.6% to approximately 1.9%. Looking at the vertical spread of the aviation induced O3 showed that downwards transport of O3 drives global distributions, creating large O3 mixing ratios in the free troposphere under the flight altitudes. Furthermore, the analysis of the vertical spread showed that the percentage contribution of aviation to local O3 mixing ratios is larger at ground level than at flight altitudes, showing that aviation induced O3 also contributes to air quality.

Analysing the OBE, showed that the OBE of aviation for the whole troposphere is about 4.5 Tg (O3) · Tg−1 (NOx). Further analysis showed that the OBE in the planetary boundary layer is about 2.5 Tg (O3)· Tg−1 (NOx), whereas the OBE at flight altitudes is about 1.3 Tg (O3) · Tg−1 (NOx). The larger OBE at ground level is caused by the large scale downward transport of the formed O3 and the low amount of local NOx emissions, whereas at flight altitudes, the local NOx emissions are not as small compared to the local O3 burden. Furthermore, analysis of the OBE metric also shows that the OBE of aviation is largest in the free troposphere below the flight altitudes, where it is estimated to be around 8 Tg (O3) · Tg−1 (NOx). This result highlights the large amount of O3 which is transported downwards to lower altitudes from the flight levels. Overall, the analysis using the OBE metric shows that it is most useful for emission sectors where the emissions are dominated by NOx emissions rather than carbon species which also contribute towards the production of O3. However, this attribute of the OBE metric makes it difficult to use OBE values to compare different emissions sources.

With renewed interest in the development of civil supersonic aircraft, their return in the future is becoming more ever more likely. The environmental impact of emissions in the stratosphere on climate and the ozone layer therefore needs to be explored. The stratospheric ozone levels determine the amount of harmful ultraviolet radiation reaching the Earth's surface and thus the level of risk to human health and ecosystems. Ozone response is complex, varying with emission altitude and latitude and we are currently reliant on computationally expensive chemistry-transport models to calculate chemical species concentration changes resulting from supersonic aviation emissions. This paper takes a novel approach to reduce the dependency on these models, creating data-driven dynamical systems that model the global spatiotemporal atmospheric ozone response for different emission scenarios. The dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) methods are applied to atmospheric ozone data obtained from the GEOS-Chem model, and the evolution of the dominant POD spatial modes are modelled using sparse identification of nonlinear dynamics algorithm (SINDy). We show that DMD models can reconstruct monthly global column ozone changes with root mean square errors less than 0.05 Dobson unit (DU) for a period of three years. Predicting the global mean column ozone changes for the years beyond the period used to construct the models, results in errors less than 0.12 DU. Independent DMD models at two different altitudes can be interpolated to produce estimates for ozone response at an intermediate altitude. These methods can serve as a basis for low dimensional surrogate models that can be used to evaluate chemical species concentrations changes as a result of supersonic aviation emissions.

Minimisation of the climate impact of flights in the short term may be achieved by contrail avoidance. This involves avoiding ice supersaturated regions (ISSRs), which cause contrails to persist. ISSRs occur when the relative humidity over ice is above 1, but numerical weather prediction (NWP) models struggle to predict such regions. Below 235.15 K, solely ice supersaturation can be considered. This study validates the prediction of ISSRs in the ERA5 reanalysis using IAGOS in-situ measurements from a pressure level (350 to 175 hPa), seasonal, yearly (2011 to 2022) and regional perspective. Validation of ERA5’s temperature showed a cold bias in all extratropic regions and at low altitudes in the tropics, which can result in ERA5 predicting ISSRs when none are observed. Increasing temperature deviations between IAGOS and ERA5 were observed between the years 2020 and 2022. In terms of relative humidity over ice, ERA5 and IAGOS showed good agreement below ice supersaturation, but ERA5 struggled in simulating values close to or above a value of 1. For the latter conditions, ERA5 showed a dry bias in the extratropics below the tropopause. A moist bias was found in some regions and seasons, including South Asia, in June, July, and August. This may be due to inconsistencies in the modelling of the atmospheric ice content in ERA5. Years 2013 and 2014 also showed a possibility of a moist bias in ERA5 in some regions, but this was most likely the result of regional changes in sampling by IAGOS. The moist bias caused false detection of ISSRs in ERA5. Meanwhile, the dry bias led to an underestimation of the occurrence of ISSRs, with 25 to 50% remaining undetected. This causes an overestimation of no persistent contrail regions and an underestimation of persistent contrail and reservoir regions. When ERA5 overestimates the ISSR occurrence, it leads to an overestimation of persistent contrail and reservoir regions but an underestimation of no persistent contrail regions. Hence, improvements are necessary to successfully avoid or ensure unnecessary avoidance of ISSRs, where the latter could cause an increase in the climate impact of flights. Improvements should be done before implementation in contrail avoidance strategies, otherwise the benefit of such strategies may be defeated.